Spectral Analysis of Aerial Light Field for Optimization Sampling and Rendering of Unmanned Aerial Vehicle

Qiuming Liu, Yichen Wang, Ying Wei, Lei Xie, Changjian Zhu, Ruoxuan Zhou
{"title":"Spectral Analysis of Aerial Light Field for Optimization Sampling and Rendering of Unmanned Aerial Vehicle","authors":"Qiuming Liu, Yichen Wang, Ying Wei, Lei Xie, Changjian Zhu, Ruoxuan Zhou","doi":"10.1109/VCIP56404.2022.10008878","DOIUrl":null,"url":null,"abstract":"The aerial light field (ALF) can render higher quality images of large-scale 3D scenes. In this paper, we apply the ALF technology to study the image captured and novel view rendering of unmanned aerial vehicle (UAV), which exists some problems, such as large scene and depth of field. First, we design an ALF sampling model using spectral analysis of Fourier theory. Based on the ALF sampling model, the exact expression of ALF spectrum is derived. By the spectral support of ALF, we analyze the influence of pitch angle on light field sampling and its bandwidth. Particularly, the bandwidth of ALF can be applied to determine the minimum sampling rate for UAV. Additionally, we design a reconstruction filter that is related to pitch angle to render novel views of UAV. Finally, our experiments show that our sampling and rendering methods can improve the rendering quality of UAV novel view rendering.","PeriodicalId":269379,"journal":{"name":"2022 IEEE International Conference on Visual Communications and Image Processing (VCIP)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Conference on Visual Communications and Image Processing (VCIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VCIP56404.2022.10008878","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The aerial light field (ALF) can render higher quality images of large-scale 3D scenes. In this paper, we apply the ALF technology to study the image captured and novel view rendering of unmanned aerial vehicle (UAV), which exists some problems, such as large scene and depth of field. First, we design an ALF sampling model using spectral analysis of Fourier theory. Based on the ALF sampling model, the exact expression of ALF spectrum is derived. By the spectral support of ALF, we analyze the influence of pitch angle on light field sampling and its bandwidth. Particularly, the bandwidth of ALF can be applied to determine the minimum sampling rate for UAV. Additionally, we design a reconstruction filter that is related to pitch angle to render novel views of UAV. Finally, our experiments show that our sampling and rendering methods can improve the rendering quality of UAV novel view rendering.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
面向无人机优化采样与绘制的航空光场光谱分析
航空光场(ALF)可以渲染高质量的大尺度三维场景图像。本文将ALF技术应用于无人机的图像捕获和新视图绘制,解决了无人机场景大、景深大等问题。首先,我们利用傅立叶理论的频谱分析设计了ALF采样模型。在ALF采样模型的基础上,导出了ALF谱的精确表达式。利用ALF的光谱支持,分析了俯仰角对光场采样及其带宽的影响。特别是,ALF的带宽可以用于确定无人机的最小采样率。此外,我们设计了一个与俯仰角相关的重建滤波器,以呈现无人机的新视角。最后,实验表明,我们的采样和绘制方法可以提高无人机新颖视图绘制的绘制质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
CdCLR: Clip-Driven Contrastive Learning for Skeleton-Based Action Recognition Spectral Analysis of Aerial Light Field for Optimization Sampling and Rendering of Unmanned Aerial Vehicle Near-lossless Point Cloud Geometry Compression Based on Adaptive Residual Compensation Efficient Interpolation Filters for Chroma Motion Compensation in Video Coding Rate Controllable Learned Image Compression Based on RFL Model
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1